Google Finance Manager: Anything that can be automated, we strive to automate it

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Google is working to automate as many finance tasks as possible as it seeks to reduce the amount of manual work its employees have to do.

The Mountain View, Calif.-based software giant uses a combination of tools including artificial intelligence, automation, cloud, data lake and machine learning to manage its financial operations and offers programs and other training to its employees.

The CFO Journal spoke with Kristin Reinke, Vice President and Chief Financial Officer of Google, about these new technologies and how they speed up the quarterly close, the use of spreadsheets in finance and the things that cannot be automated. This is part four of a series that focuses on how CFOs and other executives are digitizing their financial operations. Edited excerpts follow.

Kristin Reinke, Chief Financial Officer at Google.


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WSJ: What are the key elements of your digitization strategy?

Christine Reinke: We try to focus on the most important things: automation and [how] we can improve our processes, become better partners for the business, and then [reinvesting] the time we gain in the next business challenge.

WSJ: What tools do you use?

m/s. Reinke: We are using [machine learning] in just about every area of ​​finance to modernize the way we close the books or manage risk, or improve our [operating] process or working capital. Our controllers now use machine learning to close the books, using outlier detection.

The flow analysis needed to close the books used to be a very manual process. It took about a full day to put together various spreadsheets to identify these outliers. Now it takes one to two hours and the quality of the scan is improved. [We] can spot trends more quickly and diagnose outliers. There is another example in our [finance planning and analysis] organization: one of our teams has built a solution using the detection of outliers. So they combined outlier detection with natural language processing to surface anomalies in the data. We use this machine learning to help us predict and identify where we need to dig a little deeper. [Note: A flux analysis helps with analyzing fluctuations in account balances over time.]

WSJ: What remains to be done?

m/s. Reinke: One area where we are looking to improve is our forecast accuracy tool. This tool uses machine learning to generate accurate forecasts, and it outperforms manual forecasts developed by analysts in 80% of cases. The potential for automating this type of work is generating interest and excitement, but adoption of the tool itself has been slow, and our analysts have told us they want more granularity and transparency in how models are structured. We are working on these improvements to better understand and trust these predictions.

WSJ: What are the skills of the people you hire?

m/s. Reinke: We want to hire the best financial minds. In many cases, this talent is technical. They have [Structured Query Language] skills [a standardized programming language]. We have a finance academy where we offer SQL training for those who want it. We try to give our talents all the tools they need so they can focus on the needs of the business. We give them access to [business intelligence] and [machine learning] tools, so they don’t spend time on things that can be automated.

WSJ: You’ve worked in Google’s finance department since 2005. What changed when Ruth Porat became CFO of Alphabet and Google in 2015?

m/s. Reinke: When Ruth arrived, she emphasized organization and that discipline to automate where we can. She talks about this basic principle: “You can’t drive a car with mud on the windshield. Once you eliminate that, you can go much faster,” and that’s the importance of data.

WSJ: What are the next steps as you continue to digitize the finance function?

m/s. Reinke: I think there will be many more applications of [machine learning] and ensuring that we have data from across the business. We have this financial data lake that combines Google Cloud’s BigQuery [a data warehouse] with the financial data of our [enterprise resource planning system] and all kinds of business data that we will continue to feed as the company grows.

WSJ: Can you give more examples of new technologies and how they make your finance function more efficient?

m/s. Reinke: We use Google Cloud’s BigQuery and Document AI technologies to process thousands of supply chain invoices from our vendors. [Document AI uses machine learning to scan, analyze and understand documents.]

By pulling data from our ERP and other data from the supply chain system, we can take those thousands of invoices and validate against them and consistently approve [them]. Where we have outliers, we can actually redirect them to the business. It is therefore a less manual process for the company and for finance.

WSJ: Does your finance team use Excel or a similar tool?

m/s. Reinke: We use Google Sheets. Our finance teams love spreadsheets. I remember at first we had a bunch of finance Googlers using it and it wasn’t exactly what we needed. And so they worked with our fellow engineers to incorporate features and functionality to make it more useful in finance.

WSJ: Are there any tasks that will be off limits as you automate more?

m/s. Reinke: Anything that can be automated, we strive to automate. There’s so much judgment required as a finance organization, and it’s something you can’t automate, but you can automate the more routine activities of a finance organization by giving them these tools.

WSJ: Do you have any other examples of things that cannot be automated?

m/s. Reinke: When you sit down with the company and solve a problem they are having, you can never automate that. This type of interaction will never be automated.

WSJ: How many people work in your finance organization?

m/s. Reinke: We do not disclose the size of our teams within Google.

Write to Nina Trentman at Nina.Trentmann@wsj.com

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